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Summary

The Tourism & Recreation goal is calculated using coastal employment data from the National Ocean Economics Program.

Data source

National Ocean Economics Program (NOEP)

Downloaded: Manually downloaded by state from website on May 9, 2019.
Description: Total number of jobs and wages per sector for RI, ME, MA, CT, NY and NH counties from 2005 to 2016. The data also include number of establishments and GDP for each sector - state - year.
Native data resolution: County level
Time range: 2005 - 2016
Format: Tabular

NOTES

The data was initially cleaned in the clean_noep_data.R script.

Data cleaning

Read in the cleaned NOEP data (held in the Livelihoods folder) and select the Tourism & Recreation Sector only. We are only interested in number of jobs so we can remove the other metrics.

Visualize

Meta-analysis

To identify some inconsistencies I see in the data, I’m going to take a look at the reported Tourism & Recreation employment at both the county level and statewide. One would expect that the sum of the county employment values would equal what is reported for the “Statewide” employment values. It seems that this is not the case.

As with the jobs data layer created for Livelihoods, we see clear discrepancies in the dataset between the total number of jobs reported at the state level (red lines) and the sum of all employment numbers at the County level (blue lines) even when filtered just to the Tourism & Recreation sector. Massachusetts shows near-parallel trends in both county and statewide jobs so I am comfortable using the county level data. Since Massachusetts is split into two regions for this assessment, we will need to keep the county resolution of this data. New Hampshire and New York have identical time series so we can safely use the State data there. Rhode Island and Maine show low employment numbers in earlier years when adding up at the county level. This could be due to a lack of data. For example, Saghadoc county in Maine has no data up until 2010, when the jump happens. This suggests we should use the statewide data for Maine and Rhode Island.

There is a weird gap in the Connecticut State time series. I would like to use the State level information for Connecticut, but I will have to gap fill the NA from 2007.

Gapfilling Connecticut data

Gapfilling Connecticut’s statewide TR data. I’m going to do a simple linear interpolation between the years 2007 and 2009. Since the trend is increasing over the entire time series, I am comfortable using this simplified approach.

Now add back in to the noep_data set.

Calculate job growth

We want to calculate job growth rate in the Tourism & Recreation sector for each year. To do this, we take the annual employment and divide it by the average employment of the previous 3 years. Since our dataset begins in 2005, we can not get growth rates for the years 2005-2007.

I also save an intermediate file, coastal_jobs_data.csv, which shows the actual employment numbers for each year and the average of the previous 3 years.

References

National Ocean Economics Program. Ocean Economic Data by Sector & Industry., ONLINE. 2012. Available: http://www.OceanEconomics.org/Market/oceanEcon.asp [3 July 2018]